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所在平台: Udemy |
课程主页: https://www.udemy.com/course/artificial-intelligence-game-playing-agents/
课程评论：没有评论
**课程名称：** 人工智能 II - 游戏智能体 **课程概述：** 本课程致力于教授开发人工智能游戏程序所使用的概念、原理和实践。游戏是人类展示智慧和技巧的平台。随着人工智能在各个领域的拓展，旨在构建在体力和认知能力上与人类匹敌的机器，人工智能游戏程序已成为新兴技术领域之一。1997年，IBM“深蓝”击败国际象棋特级大师加里·卡斯帕罗夫，被视为人机对抗历史性的开端。 课程将从智能体结构简介开始。我们将深入探讨两人零和极小极大（Min-Max）游戏，其中一方的胜利即是另一方的失败。极小极大算法旨在探索所有可能性，评估最坏情况下的保证收益，并选择最佳移动。课程还将扩展到多人游戏，此时效用函数成为一个维度为k（玩家数量）的向量。 为了提高决策速度，我们将学习Alpha-Beta剪枝技术，通过剪除搜索树中不必要的分支来优化效率。鉴于需要探索的节点数量呈指数级增长，我们将开发有意义的启发式方法，设计算法以截断部分分支，从而获得近似的实时解决方案。 课程还将涵盖纯粹依赖玩家技能的技巧类游戏，以及结合随机实验来决定玩家行动顺序和方式的随机游戏。我们将深入研究随机游戏及其选择最佳移动的算法。 最后，课程还将讨论完全可见游戏（双方玩家对游戏状态完全了解）和部分可观察游戏（一方玩家的游戏棋盘对另一方隐藏）的区别。针对部分可观察游戏，我们会探讨使用统计方法来获得有意义的解决方案。课程还将介绍一些最先进的游戏程序和现代方法。
The objective of this course is to teach the concepts, principles, and practices used to develop artificial intelligence-based game programs.Games are played by people to prove the intelligence and skill of the players. As Artificial Intelligence spans across domains to build machines as capable as humans, both physically and cognitively, Artificial Intelligence-based Games programs is one of the emerging areas in technology. In 1997, the first Game playing machine, IBM Deep Blue defeated the Russian Chess Grandmaster, Garry Kasparov it was looked at as a historic beginning of the competition between machines and man. In this course, we begin with a brief introduction to the structure of an Intelligent Agent. We begin with a simple two-person Min - Max zero-sum game in which the win amount of one player is the loss of the other. The min-max algorithm aims at exploring all possibilities estimating the worst-case guaranteed amount and choosing the move. We also extend to multiplayer games where the utility function is a vector of dimension k (number of players). Alpha Beta Pruning increases the speed in decision making, by pruning some unwanted branches of the search tree. As the number of nodes to be explored is exponentially many, we try to develop some meaningful heuristic approach to develop an algorithm to cut off a few branches and get some imperfect real-time solutions. Skill-based games are played purely with the skills of players. Stochastic games combine some stochastic experiments to decide who will do the next move and how. We also deal with stochastic games and the algorithms to choose the next move. Some games are fully visible by both the players while in some games, a player's board is hidden from the other. Partially observable games use statistical methods to arrive at meaningful solutions. We also discuss some state-of-the-art game programs and modern approaches.
